3D道路车道分类与改进的纹理图案和优化的深度分类器
Bhavithra Janakiraman1, Sathiyapriya Shanmugam2, Rocío Pérez de Prado3
1Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi 642003, India.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种新的两相方法,用于自动驾驶汽车的3D车道检测. 该方法提高了道路和车道分类的准确性,使用双向封闭的循环单元和自我改进的蜜优化.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 自主系统 自主系统
背景情况:
- 准确的道路和车道理解对于自动驾驶至关重要,但目前的感知方法面临局限性.
- 3D车道检测,估计精确的可驾驶车道位置,是自动驾驶汽车的一个关键研究领域.
研究的目的:
- 通过3D图像提出一种新的二相技术,用于使用3D图像进行3D车道检测.
- 提高道路/非道路和车道/非车道分类的准确性.
主要方法:
- 第一个阶段:使用本地文本XOR模式 (LTXOR),本地Gabor二进制模式历史图序列 (LGBPHS),中位三进制模式 (MTP) 特性与双向封闭反复单元 (BI-GRU) 的道路/非道路分类.
- 第二阶段:使用类似特征的车道/非车道分类与优化的BI-GRU,其中重量通过自我改进的蜂大优化 (SI-HBO) 进行优化.
主要成果:
- 拟议的BI-GRU + SI-HBO实现了0.946 (db1) 的精度.
- 对于BI-GRU + SI-HBO的最佳准确性达到了0.928,超过了标准的蜂大优化.
- 与其他优化方法相比,自我改进的蜂大优化 (SI-HBO) 显示出更高的性能.
结论:
- 开发的两相方法有效地提高了自动驾驶汽车的3D车道检测能力.
- BI-GRU与SI-HBO的整合为提高车道检测系统的准确性和稳定性提供了一个有希望的方向.
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